AI Genogram Maker — Build & Analyze Simple | AutoGenogram
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Detail Information
What
AutoGenogram is an AI genogram maker and analyzer that turns plain-language family relationship descriptions, or uploaded family images, into structured genograms. It is aimed at therapists, social workers, students, researchers, and families who need faster diagram creation and basic pattern analysis without manual drawing. It appears positioned as an accessible web-based tool for family systems mapping, coursework, and clinical documentation support.
Features
- AI genogram generation from text: Users can describe family relationships in plain English and the system builds a professional genogram in seconds.
- Pattern analysis across generations: The product analyzes emotional patterns, conflicts, and behavioral trends to help users interpret family dynamics.
- Interactive online editor: A drag-and-drop editor allows users to adjust the generated genogram instead of rebuilding it manually.
- Clinical and relationship symbols: The paid plan includes a full clinical symbol library and 26 relationship types for more detailed family mapping.
- Export and multi-generation support: Users can export diagrams as PNG, PDF, or SVG, and higher-tier plans support layouts spanning 3, 4, or more generations.
Helpful Tips
- Treat the AI output as a starting draft; for clinical, educational, or research use, manual review is still important before sharing or filing.
- Check whether the free plan limits are sufficient, since it includes only 3 AI generations per month and 1 saved project.
- If symbol accuracy and relationship detail matter, confirm that the higher-tier symbol library and relationship types match your workflow requirements.
- The page mentions privacy and shows “Encrypted,” but it does not provide detailed handling, storage, or governance information in the provided content, so sensitive-use teams should verify that separately.
OpenClaw Skills
A likely OpenClaw use case is a workflow that collects family-history notes from interviews, converts them into structured prompts for AutoGenogram, and then routes the resulting diagrams and summaries into case preparation, teaching materials, or research documentation. Skills could plausibly include intake-note normalization, family-relationship extraction, genogram QA review, and session-summary generation. If no native integration is stated, this should be treated as a workflow-layer opportunity rather than a confirmed built-in connection.
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